Digital imaging systems, including cameras that obtain still and video data, include an image sensor and a color filter. The image sensor itself is capable of sensing intensity of radiation at each pixel of the sensor, which ranges into tens of millions of pixels for modern cameras. Color channels must be created to determine colors sensed. This is the role of the color filter. The color filter is patterned to provide three separate color channels, e.g., red (R), green (G), and Blue (B) channels. Each pixel of the image sensor is filtered by the color filter to receive only one of the three colors. Accordingly, each pixel does not physically record the full color spectrum incident upon that pixel. The digital imaging system therefore includes a processing pipeline to process data from the image sensor. A critical initial function of the processing pipeline as used in known imaging systems ranging from inexpensive digital cameras to the highest level professional model SLR and mirrorless cameras is recovering the fully color spectrum at each pixel of the image sensor. This process is known as de-mosaicing, and is conducted as a first stage of imaging processing.
The de-mosaicing obtains a full-color image, including a set of complete red, green, and blue values for each pixel. Note that the memory (comparing to the original raw sensor image size), as well as implementation complexity, is increased by a factor of 3 (three times the number of pixels to process). This de-mosaicing process is required to render raw images into a viewable format.
A widely used color filter is known as a Bayer filter. The de-mosaicing process when the Bayer filter is employed is sometimes referred to as debayering. The process is complex but also must be conducted at high speed. Modern imaging systems are capable of processing many images per second. Rendering of these images, such as via viewfinder requires de-mosaicing. Different camera manufacturers and image processing software systems employ different techniques for de-mosaicing. All are designed to permit rendering of a full image in real time or close to real time.
Imaging systems also include noise removal. An important noise source is haze, and many systems include de-hazing in an image pipeline. The haze in images and videos can impede greatly the clarity of the images. This inhibits the appearance of the images to a human observer, and provides images with less than desirable clarity.
Haze can also interfere with intelligent use of an acquired image by electronic systems that benefit from image clarity. State of the art image systems analyze full color images. State of the art approaches for single image de-hazing are disclosed in K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, December 2011; He, Sun, & Tang, “Single Image Haze Removal Using Dark Channel Prior,” Proc IEE Conf. Comput. Vist Pattern Recognit. (CVPR), pp. 1956-63 (2009); K. B. Gibson and T. Q. Nguyen, “Fast single image fog removal using the adaptive wiener filter,” in Proceedings of Int. Conf. on Image Processing, 2013, pp. 714-718.
Known image processing engines have focused on the recovery of the full color data as an initial critical step. The de-mosaicing is viewed in the art as a critical step for generation of color images from raw sensor data. De-hazing, transforms, and other forms of image correction are conducted after de-mosaicing. Normally, the only processing conducted prior to de-mosaicing includes color scaling and black level adjustment. From an image sensor, a conventional camera pipeline conducts an analog to digital conversion from the sensor that creates a set of raw sensor data. Linear color scaling and black level adjustment are then conducted. De-mosaicing follows and then de-hazing and other corrections, such as nonlinear corrections are applied.
De-hazing is a post-processing (de-mosaicing) step to enhance visibility in a conventional digital camera processing pipeline. In presence of haze or fog, the radiance xpε3 from an object is attenuated by atmospheric scattering corresponding to distance from the camera to the object, at a spatial location p. When the scattered light aε3 along an atmospheric path combines with the attenuated object radiance, the captured scene radiance pε3 is degraded asp=tpxp+(1−tp)a 
In the above expression, tpε is transmission, which is exponentially decayed with distance and invariant to wavelengths. The degraded visibility is classically improved by statistically estimating scene distance in the above expression and by adjusting scene radiance according to the scene distance. See, e.g., Philippe & Hautiere, “Fast Visibility Restoration from a Single Color or Gray Level Image,” IEEE Int. Conf. Comput. Vis (ICCV), pp. 2201-2208 (2009); Gibson & Nguyen, “Fast Single Image Fog Removal Using the Adaptive Wiener Filter,” Proc. IEEE Int. Conf. Image Processing (ICIP), pp. 714-728 (2013); He, Sun, & Tang, “Single Image Haze Removal Using Dark Channel Prior,” Proc IEE Conf. Comput. Vist Pattern Recognit. (CVPR), pp. 1956-63 (2009). These approaches measure statistics of images (such as dark channels) in a single foggy image.